Prediction of Mechanical Properties and Optimization of Friction Stir Welded 2195 Aluminum Alloy Based on BP Neural Network

Friction stir welding (FSW) is regarded as an important joining process for the next generation of aerospace aluminum alloys. However, the performance of the FSW process often suffers from low precision and a long test cycle. In order to overcome these problems, a machine learning model based on a b...

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Bibliographic Details
Published in:	Metals (Basel ) Vol. 13; no. 2; p. 267
Main Authors:	Yu, Fanqi, Zhao, Yunqiang, Lin, Zhicheng, Miao, Yugang, Zhao, Fei, Xie, Yingchun
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-01-2023
Subjects:	Alloys Aluminum aluminum alloy Aluminum alloys Aluminum base alloys Artificial neural networks Back propagation Back propagation networks Friction stir welding Heat Machine learning Mapping Mathematical models Mechanical properties Metallurgy neural network Neural networks Optimization Robots Specialty metals industry Taguchi methods Tensile strength Variance analysis Welding Welding parameters Yield stress China
Online Access:	Get full text
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